Retail Data Analyst

WGB Group is looking for a Data Analyst with strong backend and ETL skills to join our Business Intelligence & Analytics team. This is a hands-on technical role supporting the infrastructure behind how we deliver retail insights to national clients and internal stakeholders. If you’re early in your analytics career and want to grow in the direction of BI, analytics, data engineering, reporting automation, and scalable SQL development, this is a strong entry point.

About the Role:

You’ll help build, maintain, and optimize the data pipelines and structures behind our reporting and dashboarding platforms. Our work touches national home improvement retailers and CPG clients — and this role ensures that the systems behind the scenes are accurate, efficient, and ready for scale.

Key Responsibilities:

• Own and enhance ETL pipelines for weekly POS and retail performance reporting
• Write complex SQL queries to transform and aggregate large retail datasets
• Work with APIs, flat files, and cloud-based data sources to automate ingestion and processing
• Collaborate with analysts and account teams to translate business questions into data solutions
• Support and document schema development, table relationships, and logic structures
• Assist with Tableau data source prep, extracts, and performance tuning
• Proactively identify opportunities to improve pipeline reliability, speed, or structure

What You’ll Gain:

• Real-world experience supporting analytics workflows in a retail/CPG environment
• Deep exposure to retail data sources (POS, inventory, promotions, velocity)
• Mentorship in both technical skill-building and business application
• A clear path into more senior BI engineering, analytics, or data product roles
• Opportunities to influence how our firm scales its data delivery infrastructure

Qualifications:

• 1–3 years of experience in data analytics, data engineering, or BI operations
• Fluent in SQL and understand how to work with relational databases
• Experience with ETL tools (e.g., Python, Alteryx, Airflow, etc.)
• Understanding of table structure, joins, and how to optimize queries for performance
• Comfortable working across raw files, APIs, and cloud databases
• Organized, reliable, and detail-oriented with a bias for clean and well-documented work
• Exposure to Tableau or another front-end BI tool (preferred)